Relevance
2/10
Low direct relevance to quantitative trading. The paper focuses on physical climate risk propagation through supply chains and firm-level adaptation, not on financial market microstructure, asset pricing, or trading strategies. However, the systemic cascade diagnostics and supply-chain disruption modeling could indirectly inform sector rotation strategies, climate-risk-adjusted portfolio construction, or ESG factor investing. The framework's emphasis on indirect disruption borne by never-hit firms could be relevant for identifying hidden supply-chain vulnerabilities in equity portfolios.
Implementation Complexity
7/10
Moderately high complexity: requires spatial grid management (1440×720 cells), multi-agent scheduling (firms, households), geospatial hazard raster integration (rasterio, GeoPandas), Leontief production with phased within-period markets, EWMA-based adaptation state tracking, matched-seed ensemble orchestration, and stock-flow accounting. However, the codebase is modular (model.py, agents.py, run_simulation.py), uses standard Python libraries (Mesa, NumPy, pandas), runs on desktop hardware, and provides CLI-driven scenario configuration. The conceptual complexity of the adaptation mechanism and cascade diagnostics adds to implementation difficulty.
Reproducibility
5/5
Highly reproducible: open-source Python code (BSD 3-Clause License) on public GitHub, JSON parameter-file configuration, matched-seed ensembles, self-describing Meta_* metadata fields in all outputs, fixed random seeds, command-line overrides for experimental controls, and explicit warm-up/ensemble/sensitivity workflows. Hazard rasters sampled lazily at agent locations. Core source under 1 MB. Runs on standard desktop hardware.